Medical image recognition, segmentation and parsing : machine learning and multiple object approaches – Ebook PDF Instant Delivery – ISBN(s): 9780128025819,9780128026762,0128025816,0128026766

Product details:

• ISBN-10 ‏ : ‎ 0128025816
• ISBN-13 ‏ : ‎ 978-0128025819
• Author: S. Kevin Zhou

This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image.

Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects.

Table contents:

Chapter 1 – Introduction to Medical Image Recognition, Segmentation, and Parsing

Part 1: Automatic Recognition and Detection Algorithms

Chapter 2 – A Survey of Anatomy Detection

Chapter 3 – Robust Multi-Landmark Detection Based on Information Theoretic Scheduling

Chapter 4 – Landmark Detection Using Submodular Functions

Chapter 5 – Random Forests for Localization of Spinal Anatomy

Chapter 6 – Integrated Detection Network for Multiple Object Recognition

Chapter 7 – Organ Detection Using Deep Learning

Part 2: Automatic Segmentation and Parsing Algorithms

Chapter 8 – A Probabilistic Framework for Multiple Organ Segmentation Using Learning Methods and Level Sets

Chapter 9 – LOGISMOS: A Family of Graph-Based Optimal Image Segmentation Methods

Chapter 10 – A Context Integration Framework for Rapid Multiple Organ Parsing

Chapter 11 – Multiple-Atlas Segmentation in Medical Imaging

Chapter 12 – An Overview of the Multi-Object Geometric Deformable Model Approach in Biomedical Imaging

Chapter 13 – Robust and Scalable Shape Prior Modeling via Sparse Representation and Dictionary Learning

Part 3: Recognition, Segmentation and Parsing of Specific Objects

Chapter 14 – Semantic Parsing of Brain MR Images

Chapter 15 – Parsing of the Lungs and Airways

Chapter 16 – Aortic and Mitral Valve Modeling From Multi-Modal Image Data

Chapter 17 – Model-Based 3D Cardiac Image Segmentation With Marginal Space Learning

Chapter 18 – Spine Disk and RIB Centerline Parsing

Chapter 19 – Data-Driven Detection and Segmentation of Lymph Nodes

Chapter 20 – Polyp Segmentation on CT Colonography

Chapter 21 – Detect Cells and Cellular Behaviors in Phase Contrast Microscopy Images

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